Pricing an Entire NFT Collection Is Challenging, Can We BUIDL for It?
CMC Research Pricing an Entire NFT Collection Is Challenging, Can We BUIDL for It?

2 months ago

In the second column for CMC, explores why pricing NFTs, whether it’s 1 of 1s, or within an NFT collection, is extremely challenging. Pricing an Entire NFT Collection Is Challenging, Can We BUIDL for It?


By: Jeremy Seow (@CollectionWeb3), Co-founder, VP Product of and

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Pricing of NFTs: Price vs Value

The concept of “price” is often mixed up with the “value” of an asset. Described simply, the price of an asset can be described as the quantity of payment given by the buyer to the seller of the asset, while value is a more subjective opinion of how much the asset means to the individual.

In other words, trading of an asset occurs when a seller thinks that the value of the asset is lower than the selling price of the asset, and vice versa for a buyer. Only rational buyers will purchase an asset where they think the value is higher than what it is currently priced at.

The focus of this article will be on pricing of NFTs and the difficulties that arise from it, as well as discussing the implications (and need) of better protocols to drive the NFTfi space forward!

The Difficulty of Pricing NFTs

With assets that are publicly traded (on exchanges), we can easily determine the price by either looking at the last transacted price, or by looking at the order book of the asset, where we see the price that buyers are willing to pay, or the price that sellers are willing to transact at.

In the example above, taking a screenshot of the current BTC/USD market on Binance, we can quickly see the 3 pieces of information required to make a determination of the “Price of Bitcoin”:

1. The Bid - the price buyers are willing to buy bitcoin;

2. The Ask - the price sellers are willing to sell bitcoin;

3. The Last Done - the last transacted price of bitcoin.

While price determination of fungible assets (shares, cryptocurrencies, forex etc.) are largely solved, huge difficulties arise when trying to apply the same framework to non-fungible assets.


As the term implies, a non-fungible asset necessarily means that each individual asset, while similar to others in the same collection, must be recognised as a unique asset. This makes it challenging for price determination to happen over a Central Limit Order Book (described above), as we cannot assume that these unique assets, even in the same collection, are interchangeable (the exact definition of fungibility). This problem has been present before the advent of NFTs, where we look towards the pricing of rare art, fine whiskeys, luxury watches and even real estate. In most of these cases, an “estimation of price” can be done by looking at the current listings on a marketplace, coupled with the historical transacted prices of similar assets in the same genre or category, but ultimately, proper valuation can only be done with an in-depth appraisal.

With this in mind, there are methods employed in the NFT space where a quick “estimation of price” is needed.

Floor Price

The floor price of a collection is the most oft-cited metric to report the price of a collection. Floor price refers to the lowest price that a seller is willing to accept for his NFT that belongs in the collection. All marketplaces and aggregators in the NFT ecosystem have adopted the floor price as the default way to quickly track the value of NFT collections.

This method presents a few limitations for determining the price of an NFT.

1. This only reflects the lowest price that a seller is willing to accept for an NFT in the collection.

Floor price does not take into account how much a buyer is willing to pay for an NFT in the collection. The difference between these 2 prices can be huge (the spread) and hence, could result in a relative inaccurate representation of price.

2. The floor price is a 1-dimensional metric that has its limitations when pricing NFTs with more peculiar and demanded traits.

The result of the acceptance of the use of floor price has led to the creation of a subcategory of “floor NFTs” within a collection. Traders refer to this “class” of NFTs as the lowest common denominator of the entire collection (i.e, the most common NFTs in the collection, making them worth the least). Those with more demanded traits, like the “Solid Gold” trait in the Bored Ape Yacht Club collection will be worth a lot more, and utilizing the floor price alone to price a “Gold Ape” would be meaningless. Having only the floor price of a collection to price the most premium assets within the collection is akin to using the cheapest watch available from Patek Philippe (the Calatrava, worth $3000)[1] to try and estimate the price of the rarest Sky Moon Turbillion collection, valued at over $8m![2]

In addition, the rarer the NFT, the harder it is to determine the price of it as the amount of data points (lowest price a seller is willing to sell, highest price a buyer is willing to buy, the last transacted price of a similar NFT) diminishes significantly.

“Offer” Price

The “offer” price for an NFT refers to the price a buyer is willing to pay for either a) any NFT within the collection (a collection offer) or b) a specific NFT. The adoption of the word “offer” is interesting as in traditional trading parlance, this term is used to describe the price that a seller is willing to accept for his asset. In the NFT world, this is not the case as it denotes the bids in the market - something for traders new to NFTs to take note of.
The offer price is another important data point that can be used to price NFTs, as it represents what a buyer is willing to pay. In a bear market, this data point is arguably a more accurate reflection of price as it shows the immediate liquidity that an NFT can realize. The converse can be said of the floor price, as in a bull market, that represents the cheapest NFT a collector can accumulate immediately.
A limitation of putting a collection offer is the inability for the collector to have any control over what he is buying. By placing a collection offer, he implicitly agrees to buy any NFT (within the collection) that a seller is willing to liquidate. This simple assumption means that the offer price of an NFT collection will always be lower than the floor price of the collection, or a risk-free arbitrage exists.

The Spread

With the collection floor and offer price, we can construct a more precise determination of price of the most common NFT in the collection, as we can assume the answer lies somewhere in between what a buyer is willing to pay and a seller is willing to accept. This range is called the “Bid-Ask Spread”, and the mid-point is often used as a fair way to price assets. In fact, most traditional market makers utilize the mid-point as one of the key variables in their market-making algorithms.

However, in the NFT world, the Bid-Ask Spread is relatively wide, compared to fungible assets like bitcoin and Ethereum. Here’s a quick table visualizing the spreads (at the time of writing) of some of the most popular NFT collections, as seen on Opensea[3]:

As a quick comparison, most of the fungible assets that have a comparable market capitalisation ($100m to $700m market cap) that trade on centralised exchanges, will have spreads of between 0.2-0.6%, effectively less than 10 times the spread of these non-fungible assets. This difference in spread is one of the key challenges of being able to accurately determine the price of the lowest common denominator in a collection.

Machine Learning Price Oracles

With the available market data discussed above, coupled with trait-rarity as an additional model input, some projects have developed machine-learning appraisal models to help enhance the pricing efficiency of NFT markets.

Upshot has just recently announced a public release of their NFT appraisal infrastructure, where they utilize all available data to give an “industry-leading accuracy of 3-10% Median Absolute Percentage Error”. This means, half the time, the appraisal can predict within a 3-10% range[4], the next sale price of the NFT appraised.
Spicyest provides a similar service, looking at listing data, sales and traits, combined with a machine learning model, to output an appraised price for the NFTs that they cover. The model produced is marketed as having a 5-10% median error[5], which is very similar to what Upshot provides.

With this, we can start to appreciate a somewhat close-knit relationship between the spread of the asset and how “accurate” appraisal prices can be. Is the answer to more precise pricing therefore contingent upon reducing the spreads of the NFT markets?

Improving the Spreads of NFT Markets

As we discussed in our previous article, innovations are happening in the space to decrease the spreads of NFT markets. The north star is clear - incentivise liquidity providers on both the buy and sell side of markets so that traders get a fair price, at any point in time, regardless of whether they want to sell or buy an asset. If the trader is in a rush for liquidity, he should be comfortable selling into the offer price as it is not too far from what he perceives as fair value.

With narrower spreads and more liquid NFT markets, the more innovative (financial) use cases can be designed and built around it. Examples include, a more secure loan (and liquidation) platform, derivatives markets that allow for some hedging of risks to take place, or just simply a better pricing oracle that can help price the most exotic of NFTs within a collection.

To that end, encourages the narrowing of spreads by allowing liquidity providers on Sudoswap to stake their LP tokens into vaults and receive rewards. Having a more liquid NFT market will allow for a more accurate report of price of the asset, as a lower spread essentially means that buyers and sellers are more agreeable that the true price of the asset lies within a narrow bound. This will inevitably also improve the inputs that machine learning models utilize to appraise NFTs.

Improving of Spreads of Non-Floor NFTs

While the discussion above focuses on improving price discovery and spreads of an NFT collection at the floor, more innovation needs to happen in NFTFi (NFT Finance) that implicitly recognizes the unique and valued traits of NFTs that are not at the floor. The spreads that exist for non-floor NFTs make pricing these categories of NFTs even more difficult. At the time of writing, a “Solid Gold” Ape on[6] has a floor of 800 ETH and an offer for 350 ETH, a 56% spread! A “Black Suit” Ape similarly has a floor-offer spread of 51%. These spreads only add to the difficulty of creating accurate pricing models for more valued NFTs and as an extension, make them extremely difficult to be utilized efficiently as assets in other NFTFi protocols (like borrow and loan protocols).

In order for the NFTFi movement to advance, we will need to design protocols and mechanisms that recognize the value of non-floor NFTs. That way, the value of these NFTs can be better reflected, whether it is for a fairer buying or selling price, or for collateral for a loan that accounts for its unique value better.


1. Chronos24, Patek Philippe Calatrava (as Nov 24, 2022):

3., various NFT collection markets (as Nov 19, 2022):

4. Upshot is Now Open to the Public (Nov 17, 2022):

5. Spicyest: How do we get our pricing estimates?:

6., Bored Ape Yatch Club collection (as Nov 19, 2022):

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